Triple
T38174630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon cancrizans |
E1000170
|
entity |
| Predicate | hasWorkCollectionNumber |
P2011
|
FINISHED |
| Object | part of BWV 1079 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: part of BWV 1079 | Statement: [Canon cancrizans, hasWorkCollectionNumber, part of BWV 1079]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkCollectionNumber Context triple: [Canon cancrizans, hasWorkCollectionNumber, part of BWV 1079]
-
A.
hasWorkInCollection
chosen
Indicates that a work or item is included as part of a particular collection.
-
B.
hasWorkNumberType
Indicates that an entity’s work-related phone number is classified as a specific type (e.g., mobile, landline, extension).
-
C.
hasNumberWithinWork
Indicates that a specific number or numeric value is contained, referenced, or used within a particular work (such as a document, publication, or creative piece).
-
D.
appearsInWorkNumber
Indicates that an entity is featured or occurs within a specific numbered work in a series or collection.
-
E.
hasWorkNumberInCycle
Indicates that an entity is assigned a specific work number or identifier within a particular cycle or iteration.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76daaace48190a38cee37f8ce343f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fef8c3f2388190b995ec173512945a |
completed | May 9, 2026, 9:05 a.m. |
| PD | Predicate disambiguation | batch_69fef65975608190960b78d27e806d4f |
completed | May 9, 2026, 8:54 a.m. |
Created at: May 3, 2026, 4:29 p.m.